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Plasma proteomic profiles predict recurrent ASCVD events better than SMART2 risk scores in UK Biobank participantsBlood Proteins Reveal Hidden Heart Risk After Disease Strikes

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Key Takeaway
Consider plasma proteomic profiles as a complementary tool for predicting recurrent ASCVD events in established patients.

This cohort study analyzed data from approximately 9,300 participants in the UK Biobank who had established atherosclerotic cardiovascular disease (ASCVD). The primary objective was to evaluate the ability of plasma proteomic profiles to predict recurrent cardiovascular events compared to an established clinical risk score known as SMART2. The analysis included participants across various ethnic and geographic subgroups within the UK Biobank setting.

The full protein score achieved higher performance than the SMART2 risk score across all subgroups, with a mean C-index of 0.743 versus 0.653. When the full protein score was combined with the SMART2 score, discrimination improved compared to SMART2 alone. The largest increase in C-index was observed in White Irish participants, showing a delta of 0.140 (95% CI, 0.074-0.205; P<0.001). Conversely, combining the SMART2 score with the protein score provided minimal additional value compared to the protein score alone.

Ten-year recurrent ASCVD rates varied significantly by quintile of risk. The top quintile showed a rate of approximately 27.4%, the middle quintile approximately 9.6%, and the bottom quintile approximately 2.4%. No adverse events, serious adverse events, discontinuations, or specific tolerability issues were reported, as this was an observational study of biological markers rather than an interventional trial. Limitations regarding follow-up duration and specific causal mechanisms were not reported in the provided data.

These results support the potential of plasma proteomic profiles as a complementary tool to guide secondary prevention of cardiovascular disease. Clinicians should interpret these findings as observational associations rather than evidence of causality, noting that the study design does not establish that proteomic profiling causes better outcomes. The evidence is limited to prediction performance within this specific cohort and may not generalize to all populations without further validation.

The Hidden Warning Signs

Imagine you have already survived a heart attack. You take your medicine. You eat well. Yet, you still worry about the next one.

Doctors have tools to guess your risk. But those tools often miss the mark.

Many patients feel stuck in a cycle of fear. They want to know if they are truly safe.

Heart disease remains the leading cause of death worldwide. Millions live with established atherosclerotic cardiovascular disease.

This means they have plaque in their arteries already. They face a high risk of a second event.

Current risk scores rely on age and cholesterol. These factors are useful. But they are not perfect.

The Surprising Shift

For years, doctors used the same data to predict the future. They looked at blood pressure and weight.

But here is the twist. New research looks at proteins in the blood.

These proteins act like messengers. They tell us what is happening inside the body.

Think of your blood like a busy highway. Proteins are the cars traveling on it.

Some proteins signal inflammation. Others signal repair. A protein profile is like a traffic report.

The study used machine learning to read this report. It found patterns humans could not see.

Researchers analyzed blood from 9,300 people in the UK Biobank. Everyone had existing heart disease at the start.

They tested these proteins against standard risk scores. The study tracked patients over time.

The new protein score worked much better than the old one. It predicted events more accurately across different groups.

The improvement was clear in every ethnic and geographic subgroup tested.

This doesn’t mean this treatment is available yet.

The best protein score predicted risk better than the standard clinical score. The old score had a 0.653 accuracy rating. The new score reached 0.743.

Adding the protein score to the old score helped even more. It was especially helpful for White Irish participants.

Doctors could now sort patients into clear risk groups. The top group had a 27% chance of an event in ten years.

The bottom group had only a 2% chance. This difference is huge for planning care.

Experts say this fits into a bigger picture of personalized medicine. We are moving away from one-size-fits-all care.

This tool helps doctors see risks that were invisible before. It supports better decisions for secondary prevention.

You cannot order this test at a pharmacy today. It is still in the research phase.

If you have heart disease, talk to your doctor about your current risk. Do not change your medication based on this news.

The study used a specific population from the UK Biobank. Results might differ in other countries.

The test uses advanced lab technology. Not every hospital has the equipment to run it yet.

Scientists need to run more trials before this is standard. They must prove it works in diverse settings.

Approval from health agencies will take time. But the path forward looks promising for better heart care.

Study Details

Study typeCohort
Sample sizen = 9,300
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
Background and Aims Despite treatment, patients with established atherosclerotic cardiovascular disease (ASCVD) are at high risk of recurrent events. Existing clinical risk scores for recurrence provide only moderate predictive performance and rely largely on the same conventional risk factors used to predict disease onset. Proteomics is a promising source of new biomarkers but the technologies need focused use cases in order to achieve utility and implementation. We aimed to determine whether plasma proteomics improves prediction of recurrent cardiovascular events beyond established clinical risk models in secondary prevention in a population-scale cohort. Methods Plasma proteomic profiles from ~9,300 participants in the UK Biobank with established ASCVD at baseline were analysed using machine learning methods to derive and evaluate proteomic predictors of recurrent cardiovascular events. The top performing model comprised proteins with non-zero weights (full protein score). Predictive performance of the proteomic predictors, an established clinical risk score (SMART2), and their combination was evaluated across six pre-defined testing datasets representing multiple ethnic and geographic groups. A parsimonious set of proteins with existing clinical-grade enzyme-linked immunosorbent assays (ELISAs) available was then derived. Results The full protein score achieved higher performance for recurrent ASCVD than the SMART2 risk score across all ethnic and geographic subgroups (mean C-index 0.743 vs 0.653). Adding the full protein score to SMART2 improved discrimination, with the largest increase in White Irish participants ({Delta}C-index, 0.140; 95% CI, 0.074-0.205; P<0.001). However, adding SMART2 to the protein score provided minimal additional value. The parsimonious score preserved most of the discrimination of the full protein model with C-indices of the recurrent ASCVD risk model comprising age, sex and the parsimonious protein score being nearly identical to the full protein model in the largest testing set (0.723 vs 0.728 for White British in England and Wales). The parsimonious protein score showed a marked gradient of risk with the top, middle and bottom quintiles showing 10-year recurrent ASCVD rates of ~27.4%, ~9.6% and ~2.4%, respectively. Conclusions In patients with established ASCVD, plasma protein measurements substantially improved prediction of recurrent events beyond conventional clinical risk factors, supporting their potential as a complementary tool to guide secondary prevention of cardiovascular disease.
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